Optimising AI in Private Equity: Insights from Kendra Vant

With the hype surrounding artificial intelligence (AI) reaching fever pitch, private equity firms face a critical question: how can they pragmatically harness this technology to drive portfolio value? Dr Kendra Vant is a distinctive voice in AI consulting, combining executive leadership with hands-on technical expertise. 

Having spent more than seven years at major Australian SaaS companies, including Seek and Xero, Kendra has launched > 10 AI-based products to global markets, reaching millions of users. Now she's helping the broader business community explore opportunities to expand their capacity using this rapidly evolving technology.  

“I'm unusual in that I have current practitioner-level experience in building AI systems," Kendra explains. “Often, AI executives have long since left the tools behind. I've kept a foot on each side – I've always stayed abreast of the evolving technology."

Are We Ready for AI?

The business market's approach to AI reflects a complex mix of excitement, caution, and pragmatism. Kendra observes significant variation between executive teams and boards, with executives showing greater interest while boards often maintain risk-based perspectives, likely shaped by previous technology transformation failures and cost overruns.

Industry factors also drive adoption rates. Companies facing immediate disruption show stronger urgency, while those in industries where physical labour is still a significant cost recognise they have more time before AI substantially impacts operations and margins.

Across the board, many executives remain uncertain about when – and how – to get started with AI. “The only way to really learn how useful these technologies will be for your business is to have a go,” Kendra advises. “Executives of companies that are experimenting are becoming more confident in applying this technology in ways that make business sense for their industry.”

Where – and How – Do We Start?

Rather than developing new tools internally, Kendra advises most PE firms looking for productivity gains from AI to leverage existing vendor ecosystems – many software products already incorporate generative AI capabilities, from Microsoft Copilot to Google Gemini. 

“Start by evaluating your current tech stack to identify embedded AI capabilities that could drive immediate value,” she suggests. “And talk to your vendors about their roadmaps.”

Another key lies in identifying process champions within the firm. “Somewhere in your firm, you’ll have that one finance person who’s fascinated by this technology, or that analyst who finds it personally interesting.” These champions provide genuine internal evidence of where AI is ready to deliver value today.

PE firms can gain significant value when they shift the focus from internal practices to their portfolio investments, sharing AI knowledge and learnings across companies with similar needs, or bringing in specialists who can syndicate expertise across multiple investments. “Look to introduce AI to help resolve challenges that are common across the portfolio. For instance, if your PE firm invests in professional services firms, it’s likely that you have multiple high-cost people creating different documentation. AI can be used to streamline these processes with significant cost savings.”

Find the Strategic Value in AI

Kendra identifies two critical opportunities for PE firms to engage deep AI expertise. The first is to develop an investment thesis. “I don’t see enough Australian firms thinking deeply about how AI will reshape their industry or value chains,” she explains. “Even if the impact on your firm is some years off, how is your market shifting, and what new power players are emerging?”

The second opportunity is to make informed decisions about specifically where and when to use AI for value creation within portfolio companies.

And she says timing is critical when investing in AI. Unlike specialists, such as branding or pricing experts who work with companies as they are today, AI experts must position companies for where they'll be in 18 months.

“A deep AI expert will look at where the company is today – the way you capture data, store data, the permissions you gain from customers and industry partners – and manage that for use in 18 months,” she explains.

The Fractional Model Advantage

For PE firms evaluating AI talent acquisition, Kendra advocates fractional arrangements – a model gaining traction in Australia.

This approach particularly benefits smaller Australian PE firms. “It can be challenging to look at portfolios and say how do I invest in senior AI expertise cost-effectively across the business. Building an ongoing advisory relationship with somebody who knows how you invest means they can offer advice that isn't generic.”

She explains that the fractional model also allows firms to test the waters before committing to full-time expertise. “You can pay for as much expertise as you need rather than being all in or all out.. It’s often attractive to the experts too – working across a range of companies provides opportunities to apply their skills exactly when they are needed.”

Avoid Common AI Pitfalls

One frequent mistake Kendra has observed involves setting unrealistic expectations for AI appointments. “Often, a company will want transformation, but it will engage functional AI specialists who do not have the experience or professional credibility to educate and convince executives and boards of investment needs.” 

Another critical error is to treat AI implementation as purely a technology problem. “Almost always, technology is part of the solution, but the processes wrapped around it – which are about humans inside the company – also need to shift.” 

To avoid this, Kendra recommends assuming any change will be “20% technology and 80% people and processes,” then planning and investing appropriately.

Getting Started Practically

The AI hype cycle can complicate even the most disciplined investment approach. “The same rigorous due diligence that serves PE firms well in more established business areas applies equally to AI initiatives,” Kendra notes.

Rather than focusing on future AI promises, businesses should focus on current capabilities. “Let’s figure out what we could build with the Gen AI models as they exist today and whether that can save us money.”

For initial projects, she recommends the following parameters: Look for situations that will move the needle, where a human can be kept in the loop, where an answer that’s right on average is actually useful, and pick places where the workflow is reasonably forgiving. 

“We often can’t replace entire task sets, but maybe we can assist the expensive human employee by augmenting two or three workflow elements and make them 5% or 7% more efficient,” she says.

“The key lies in starting small, learning fast, and scaling what works – principles that should resonate with any experienced investor.”